The primary considerations in rice (Oryza sativa L.) production evoke improvements in the nutritional quality as well as production. Rice cultivars need to be developed to tackle hunger globally with high yield and better nutrition. The traditional cultivation methods of rice to increase the production by use of non-judicious fertilizers to fulfill the nutritional requirement of the masses. This article provokes nutritional strategies by utilization of available omics techniques to increase the nutritional profiling of rice. Recent scientific advancements in genetic resources provide many approaches for better understanding the molecular mechanisms encircled in a specific trait for its up- or down-regulation for opening new horizons for marker-assisted breeding of new rice varieties. In this perspective, genome-wide association studies, genome selection (GS) and QTL mapping are all genetic analysis that help in precise augmentation of specific nutritional enrichment in rice grain. Implementation of several omics techniques are effective approaches to enhance and regulate the nutritional quality of rice cultivars. Advancements in different types of omics including genomics and pangenomics, transcriptomics, metabolomics, nutrigenomics and proteomics are also relevant to rice development initiatives. This review article compiles genes, locus, mutants and for rice yield and yield attribute enhancement. This knowledge will be useful for now and for the future regarding rice studies. Full article

Automatic climate management enables us to reduce repetitive work and share knowledge of different experts. An artificial intelligence-based layer to manage climate in white button mushroom growing hall was presented in this article. It combines visual data, climate data collected by sensors, and technologists’ actions taken to manage climate in the mushroom growing hall. The layer employs visual data analysis methods (morphological analysis, Fourier analysis, convolutional neural networks) to extract indicators, such as the percentage of mycelium coverage and number of pins of different size per area unit. These indicators are used to generate time series that represent the dynamics of the mushroom growing process. The incorporation of time synchronized indicators obtained from visual data with monitored climate indicators and technologists’ actions allows for the application of a supervised learning decision making model to automatically define necessary climate changes. Whereas managed climate parameters and visual indicators depend on the mushroom production stage, three different models were created to correspond the incubation, shock, and fruiting stage of the mushroom production process (using decision trees, K-nearest neighbors’ method). An analysis of the results showed that trends of the selected visual indicators remain similar during different cultivations. Thus, the created decision-making models allow for the definition of the majority of the cases in which the climate change or transition between the growing stages is needed. Full article

Precision poultry farming technologies include the analysis of images of poultry flocks using cameras. In large-scale waterfowl farming, these can be used to determine the individual weight of poultry flocks. In our research in a real farming environment, we investigated the cameras fixed to the metal support structure of the barn, located above the suspended bird scales. Camera images of the bird on the weighing cell, taken from a top view, were matched to the weight data measured by the scale. The algorithm was trained on training data sets from a part of the database, and the results were validated with the other part of the database (Training: 60% Validation: 20% Testing: 20%). Three data science models were compared, and the random forest method achieved the highest accuracy and reliability. Our results show that the random forest method gave the most reliable results for determining the individual weights of birds. We found that the housing environment had a strong influence on the applicability of the data collection and processing technology. We have presented that by analyzing carefully collected images, it is possible to determine the individual weights of birds and thus provide valuable information on it. Full article

This study took the wheat grown in the experimental area of Jiangsu Academy of Agricultural Sciences as the research object and used the unmanned aerial vehicle (UAV) to carry the Rededge-MX multispectral camera to obtain the wheat scab image with different spatial resolutions (1.44 cm, 2.11 cm, 3.47 cm, 4.96 cm, 6.34 cm, and 7.67 cm). The vegetation indexes (VIs) and texture features (TFs) extracted from the UAV multispectral image were screened for high correlation with the disease index (DI) to investigate the impact of spatial resolution on the accuracy of UAV multispectral wheat scab monitoring. Finally, the best spatial resolution for UAV multispectral monitoring of wheat scab was determined to be 3.47 cm, and then, based on the 3.47 cm best resolution image, VIs and TFs were used as input variables, and three algorithms of partial least squares regression (PLSR), support vector machine regression (SVR), and back propagation neural network (BPNN) was used to establish wheat scab, monitoring models. The findings demonstrated that the VIs and TFs fusion model was more appropriate for monitoring wheat scabs by UAV remote sensing and had better fitting and monitoring accuracy than the single data source monitoring model during the wheat filling period. The SVR algorithm has the best monitoring effect in the multi-source data fusion model (VIs and TFs). The training set was identified as 0.81, 4.27, and 1.88 for the coefficient of determination (R²), root mean square error (RMSE), and relative percent deviation (RPD). The verification set was identified as 0.83, 3.35, and 2.72 for R², RMSE, and RPD. In conclusion, the results of this study provide a scheme for the field crop diseases in the UAV monitoring area, especially for the classification and variable application of wheat scabs by near-earth remote sensing monitoring. Full article

Nowadays, China’s food consumption structure is shifting from being survival-oriented to health-oriented. However, the food industry is still facing a research and development (R&D) dilemma. Scientific evaluation of an enterprise’s R&D performance can help to reduce the investment risk of R&D and promote economic benefits. This study implements the dynamic data envelopment analysis (DDEA) technique to measure and evaluate the level of R&D performance in the Chinese food manufacturing industry. Twenty-eight listed companies were selected for the study, considering the time period from 2019 to 2021. After constructing a system of inputs, outputs and carry-over indicators, overall and period efficiency scores were obtained. The results reveal that the overall level of R&D in the industry is relatively low (0.332). Average efficiency scores across years were estimated as 0.447, 0.460, 0.430 for 2019, 2020, and 2021, respectively. Lastly, this study considers the actual business situation of the industry and makes suggestions for improvement from the perspective of enterprises and the government; these anticipate aiding the food manufacturing industry to improve the performance management of R&D activities. Full article

Bacuri (Platonia insignis) is a monotype belonging to the Clusiaceae family. Of Amazonian origin, it is highly appreciated for fresh consumption, mainly due to its peculiar sensory characteristics. It is also widely used in the food industry, mainly in pulp (endocarp), used in the manufacture of beverages, jellies, and ice cream. Although the use of pulp is well established in the food sector, recently, research has turned attention to the use of other parts of the fruit and plant, especially in the therapeutics, cosmetics, and fuel sectors. Its bioactive components have been investigated for having important antioxidant, anti-inflammatory, immunomodulatory, hypotensive, cardioprotective, antiepileptic, antileishmanial, and antifungal activities, among others, mainly attributed to the presence of compounds such as xanthones, terpenes, phenolics, and fatty acids. Thus, this study aimed to gather data on the species Platonia insignis Mart. through an integrative review of the agronomic, nutritional, physical–chemical characteristics and a technological prospection about its applications. The study showed that in the last ten years there has been a significant increase in the number of patents deposited, with the prospect that with the advancement of studies on their properties, results for application in the most diverse areas will prove increasingly viable and promising. Full article

To solve the problems of high labor costs, a low weeding rate and a high seedling injury rate in the direct seeding of rice fields, this paper presents a reciprocating inter-row weeding machine for strip-seeded rice. The machine uses a combination of weeding wheels and weeding shovels to improve the efficiency of weeding between rice rows. Its reciprocating mechanism was designed and optimized. The simulation model of weeding teeth–paddy soil interaction was established in EDEM. The structural parameters of the weeding teeth were optimized, and the bending angle of the optimized weeding teeth was 55°. A prototype trial production and field tests were carried out. The results showed that the prototype’s inter-row weeding rate was between 80.2% and 85.3% and the seedling injury rate was between 3.5% and 5.1% when the prototype’s working speed was 1~3 km h⁻¹. The faster the speed of the prototype, the lower the inter-row weeding rate and the higher the seedling injury rate. Full article

Herbicides are widely used to kill weeds and increase crop production all over the world. Nevertheless, some weeds show certain structural modifications in response to herbicide application that impart mostly partial or sometimes complete tolerance to these noxious plants. The present study was focused on morpho-anatomical modifications in the root, stem, and leaves of Dactyloctenium aegyptium (L.) Willd. treated with different herbicides and to examine whether it possesses tolerance against herbicides. Two pre- and four post-emergence herbicides were applied to D. aegyptium at the recommended dose in a randomized complete block design (RCBD). Pre-emergence herbicide Bromoxynil enhanced root growth (30%), leaves per plant (3%), and leaf fresh weight (17.2%). Increased stem epidermal thickness (100%) was the most notable feature among anatomical attributes. Post-emergence herbicides generally increased stem epidermal thickness 33–56%), leaf sheath thickness (5%), and root area in roots. Other modifications included increased sclerenchymatous thickness in the stem (133–255%), and epidermal thickness (100–200%) in the leaf blade. These characters assisted D. aegyptium to cope with herbicide toxicity. Collectively, pre-emergence herbicides more effectively controlled D. aegyptium compared with post-emergence herbicides. Full article

Rising labor costs and the inefficient manual methods of cultivating red onions in Bangladesh contribute to the country’s failure to meet is own demand. Mechanizing the process of transplanting red onion seedlings will reduce the manual labor required while increasing crop yields. This paper provides an initial study of a proposed mechanized onion transplanter designed to attach to the back side of a two-wheel tractor with power tiller operated-seeder, commonly used in Bangladesh. Testing of a prototype made from these designs proves that the design is functional but requires further development for commercial/widespread use. Full article

Minimal participation in green agricultural development may be achieved via the conventional integration of agriculture and tourism, which has a minimal impact on farmers’ green output. New traits have emerged as a result of agro-tourism integration with digital empowerment. It was shown that agro-tourism integration with digital empowerment had a stronger impact on farmers’ green output than traditional agro-tourism integration, based on the construction of the dynamic information game model and the case of Xichong, Sichuan. The integration of agriculture and tourism from the perspective of digital empowerment is characterized by “data traceability” and “information diffusion”, which restrains opportunistic farmer impersonation. The feature of “knowledge sharing” promotes the progress of agricultural technology, reduces the cost of green production and increases the probability of farmers producing high-quality agricultural products. The “information matching” feature promotes the symmetry of quality information, and production and sales information at both ends of supply and demand, and raises the prices of high-quality agricultural products. The latter two features jointly enhance the willingness of honest farmers to produce green items by reducing the cost of green production and increasing the prices of high-quality agricultural products. From the perspective of digital empowerment, the integration of agriculture and tourism can further promote farmers’ green production by effectively suppressing opportunistic farmers’ fake behavior and promoting honest farmers’ green production to a greater extent. Full article

Nitrogen (N) is an important factor limiting crop productivity, and accurate estimation of the N content in winter wheat can effectively monitor the crop growth status. The objective of this study was to evaluate the ability of the unmanned aerial vehicle (UAV) platform with multiple sensors to estimate the N content of winter wheat using machine learning algorithms; to collect multispectral (MS), red-green-blue (RGB), and thermal infrared (TIR) images to construct a multi-source data fusion dataset; to predict the N content in winter wheat using random forest regression (RFR), support vector machine regression (SVR), and partial least squares regression (PLSR). The results showed that the mean absolute error (MAE) and relative root-mean-square error (rRMSE) of all models showed an overall decreasing trend with an increasing number of input features from different data sources. The accuracy varied among the three algorithms used, with RFR achieving the highest prediction accuracy with an MAE of 1.616 mg/g and rRMSE of 12.333%. For models built with single sensor data, MS images achieved a higher accuracy than RGB and TIR images. This study showed that the multi-source data fusion technique can enhance the prediction of N content in winter wheat and provide assistance for decision-making in practical production. Full article

The response of different plant growth regulators on callus induction and regeneration on three sugarcane genotypes (YT-53, CP-77-400, and NSG-59) was evaluated. Different concentrations of 2,4-D alone and in combination with other plant growth regulators (Kinetin and BAP) were used for callus induction. Kinetin along with IBA, BAP and NAA were analyzed with respect to shoot induction, while NAA and IBA were used for root induction. The best callus response in terms of number of days, callus fresh weight, and frequency in YT-53 was observed on MS media provided with 2,4-D (3 mg L⁻¹) + Kinetin (0.5 mg L⁻¹), while in NSG-59 the best response was on MS+2,4-D (4 mg L⁻¹) + Kinetin (0.5 mg L⁻¹), and in CP-77400, MS+2,4-D (5 mg L⁻¹). For shoot induction, 2 mg L⁻¹ Kinetin was found to be the best for YT-53 and NSG-59, while 1 mg L⁻¹ BAP was found to be the best for CP-77-400 in terms of number of days, shoot numbers, and shoot length. The best media for root induction in terms of number of days, root numbers, and root length was 1 mg L⁻¹ NAA + 1 mg L⁻¹ IBA for YT-53, while this was 3 mg L⁻¹ NAA for NSG-59. The highest root frequency and maximum root length in the minimum number of days was observed in CP-77-400 on MS media provided with 2 mg L⁻¹ NAA. Full article

The national-scale evaluation and modeling of the impact of agricultural management and climate change on soils, crop growth, and the environment require soil information at a spatial resolution addressing individual agricultural fields. This manuscript presents a data science approach that agglomerates the soil parameter space into a limited number of functional soil process units (SPUs) that may be used to run agricultural process models. In fact, two unsupervised classification methods were developed to generate a multivariate 3D data product consisting of SPUs, each being defined by a multivariate parameter distribution along the depth profile from 0 to 100 cm. The two methods account for differences in variable types and distributions and involve genetic algorithm optimization to identify those SPUs with the lowest internal variability and maximum inter-unit difference with regards to both their soil characteristics and landscape setting. The high potential of the methods was demonstrated by applying them to the agricultural German soil landscape. The resulting data product consists of 20 SPUs. It has a 100 m raster resolution in the 2D mapping space, and its resolution along the depth profile is 1 cm. It includes the soil properties texture, stone content, bulk density, hydromorphic properties, total organic carbon content, and pH. Full article

Narrowing the rural–urban income gap is an important challenge in achieving sustained and stable economic and social development in China. The present study investigates the role of farmers’ education and agricultural productivity growth in influencing the rural–urban income gap by applying mediation, interaction, and quantile regression models to provincial panel data of China from 2003 to 2017. Results show that, first of all, China’s agricultural productivity (TFP) continues to improve, and it is mainly driven by technical change (TC), with no significant role of technical efficiency change (TEC) or stable scale change (SC). Improving farmers’ education not only directly narrows the rural–urban income gap but also indirectly improves agricultural productivity to further narrow the rural–urban income gap. Due to differences in income sources of farmers, the corresponding impacts of farmers’ education and agricultural productivity growth on the rural–urban income gap also differ. Policy recommendations include continued investments in farmers’ education and training as well as modernization of agricultural for higher productivity growth. Full article

Assessing the quality of agricultural products is an essential step to reduce food waste. The problems of overly complex models, difficult to deploy to mobile devices, and slow real-time detection in the application of deep learning in agricultural product quality assessment requiring solutions. This paper proposes a lightweight method based on ShuffleNetV2 to identify phenotypic diseases in corn seeds and conduct experiments on a corn seed dataset. Firstly, Cycle-Consistent Adversarial Networks are used to solve the problem of unbalanced datasets, while the Efficient Channel Attention module is added to enhance network performance. After this, a $7\times 7$ depthwise convolution is used to increase the effective receptive field of the network. The repetitions of basic units in ShuffleNetV2 are also reduced to lighten the network structure. Finally, experimental results indicate that the number of model parameters are 0.913 M, the computational volume is 44.75 MFLOPs and 88.5 MMAdd, and the recognition accuracy is 96.28%. The inference speed of about 9.71 ms for each image was tested on a mobile portable laptop with only a single CPU, which provides a reference for mobile deployment. Full article